| --- |
| language: en |
| license: cc-by-4.0 |
| base_model: FacebookAI/roberta-base |
| pipeline_tag: text-classification |
| tags: |
| - english |
| - literature |
| - digital-humanities |
| - narratology |
| widget: |
| - text: "He walked through the door and shut it behind him." |
| example_title: action_space |
| - text: "From the hilltop she could see the whole valley spread out below." |
| example_title: visual_space |
| - text: "The room felt heavy and oppressive, as if the walls were closing in." |
| example_title: perceived_space |
| - text: "A table stood in the corner, covered with dusty books." |
| example_title: descriptive_space |
| - text: "She wondered whether he would ever write back." |
| example_title: no_space |
| --- |
| |
| # en-setting-classifier |
|
|
| Sentence-level classifier for the representation of space in English narrative prose. |
| Fine-tuned from [`FacebookAI/roberta-base`](https://huggingface.co/FacebookAI/roberta-base). |
|
|
| English counterpart to |
| [`katrohrb/de-setting-classifier`](https://huggingface.co/katrohrb/de-setting-classifier); |
| both models use the same five categories and the same label encoding. |
|
|
| ## Categories |
|
|
| | Label | Description | |
| |---------------------|-----------------------------------------------------------------------------| |
| | `action_space` | Space registered through a character's movement and direct physical contact | |
| | `visual_space` | Space as perceived by a character from a distance | |
| | `perceived_space` | Space as it carries mood and atmosphere | |
| | `descriptive_space` | Things and objects in space, without reference to a character | |
| | `no_space` | No spatial reference | |
|
|
| Label ids: `0 perceived_space`, `1 action_space`, `2 visual_space`, `3 descriptive_space`, |
| `4 no_space` (stored in `config.json` as `id2label`). |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| name = "katrohrb/en-setting-classifier" |
| tok = AutoTokenizer.from_pretrained(name) |
| model = AutoModelForSequenceClassification.from_pretrained(name).eval() |
| |
| sentence = "He walked through the door and shut it behind him." |
| with torch.no_grad(): |
| probs = model(**tok(sentence, return_tensors="pt")).logits.softmax(-1)[0] |
| print(model.config.id2label[int(probs.argmax())]) |
| # → action_space |
| ``` |
|
|
| Or with a pipeline: |
|
|
| ```python |
| from transformers import pipeline |
| |
| clf = pipeline("text-classification", model="katrohrb/en-setting-classifier") |
| clf("From the hilltop she could see the whole valley spread out below.") |
| # → [{'label': 'visual_space', 'score': 0.87}] |
| ``` |
|
|
| ## Citation |
|
|
| Rohrbacher, K., Nieth, B., Salin, E., Eskofier, B., Mahlberg, M. (2026). |
| How LLMs Build Fictional Worlds: Measuring Setting and Narrative Space in AI-Generated Creative Storytelling. |
| Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP). |
| To appear. Preprint: https://doi.org/10.48550/arXiv.2609.02482 |
|
|
